Leading indicator properties of US high-yield credit spreads

Nektarios Aslanidis and Andrea Cipollini ()

Working Papers from Universitat Rovira i Virgili, Department of Economics

Abstract: In this paper we examine the out-of-sample forecast performance of high-yield credit spreads regarding real-time and revised data on employment and industrial production in the US. We evaluate models using both a point forecast and a probability forecast exercise. Our main findings suggest the use of few factors obtained by pooling information from a number of sector-specific high-yield credit spreads. This can be justified by observing that, especially for employment, there is a gain from using a principal components model fitted to high-yield credit spreads compared to the prediction produced by benchmarks, such as an AR, and ARDL models that use either the term spread or the aggregate high-yield spread as exogenous regressor. Moreover, forecasts based on real-time data are generally comparable to forecasts based on revised data. JEL Classification: C22; C53; E32 Keywords: Credit spreads; Principal components; Forecasting; Real-time data.

Keywords: Sèries temporals--Anàlisi; Previsió econòmica--Models economètrics; Cicles econòmics; Processament de dades en temps real; Crèdit; 338 - Situació econòmica. Política econòmica. Gestió; control i planificació de l'economia. Producció. Serveis. Turisme. Preus (search for similar items in EconPapers)
Date: 2009
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http://hdl.handle.net/2072/15810

Related works:
Journal Article: Leading indicator properties of US high-yield credit spreads (2010)
Working Paper: Leading indicator properties of US high-yield credit spreads (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:urv:wpaper:2072/15810

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